A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle

Changran He, Guoye Wang, Zhangpeng Gong, Zhichao Xing and Dongxin Xu
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Changran He: College of Engineering, China Agricultural University, Beijing 100083, China
Guoye Wang: College of Engineering, China Agricultural University, Beijing 100083, China
Zhangpeng Gong: College of Engineering, China Agricultural University, Beijing 100083, China
Zhichao Xing: College of Engineering, China Agricultural University, Beijing 100083, China
Dongxin Xu: College of Engineering, China Agricultural University, Beijing 100083, China

Energies, 2018, vol. 11, issue 9, 1-18

Abstract: Current regenerative braking systems in electric vehicles have several problems, such as complex structures, too many control parameters, and inconsistent braking responses. To solve these problems, a control algorithm with multidisciplinary design optimization (MDO) is proposed based on the novel regenerative–mechanical coupled brake-by-wire system. A dynamic model of the novel regenerative braking system was established to analyze the mechanism of coupled braking and propose a braking torque distribution strategy. To realize a better balance between the optimum braking stability and the maximum regenerative energy recovery based on the braking torque distribution strategy and sample points, the MDO mathematical model was developed to optimize the control parameters with the collaborative optimization algorithm. The finite sample points comprising the vehicle speed, battery state-of-charge, and braking severity were obtained through an optimal Latin hypercube design and represent the overall design space. A network was established based on the sample points and the optimization results. Using this network, the in-depth characteristics of the sample points and the optimization results were obtained through supervised learning to develop the control algorithm for vehicle braking. A simulation was performed using the normal braking condition, and the simulation results demonstrated that the control algorithm has higher control precision than conventional methods and better real-time performance than online optimization.

Keywords: multidisciplinary design optimization; regenerative braking; optimum control; regenerative–mechanical coupling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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